Improving prediction interval quality : a genetic algorithm-based method applied to neural networks

Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2009, Improving prediction interval quality : a genetic algorithm-based method applied to neural networks, Lecture notes in computer science, vol. 5864, pp. 141-149, doi: 10.1007/978-3-642-10684-2_16.

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Title Improving prediction interval quality : a genetic algorithm-based method applied to neural networks
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Creighton, DougORCID iD for Creighton, Doug
Journal name Lecture notes in computer science
Volume number 5864
Start page 141
End page 149
Total pages 9
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2009-12-15
ISSN 0302-9743
Keyword(s) Neural network
genetic algorithm
prediction interval
Summary The delta technique has been proposed in literature for constructing
prediction intervals for targets estimated by neural networks. Quality of constructed prediction intervals using this technique highly depends on neural network characteristics. Unfortunately, literature is void of information about how these dependences can be managed in order to optimize prediction intervals. This study attempts to optimize length and coverage probability of prediction intervals through modifying structure and parameters of the underlying neural networks. In an evolutionary optimization, genetic algorithm is applied for finding the optimal values of network size and training hyper-parameters. The applicability and efficiency of the proposed optimization technique is examined and demonstrated using a real case study. It is shown that application of the proposed optimization technique significantly improves quality of constructed prediction intervals in term of length and coverage probability.
Language eng
DOI 10.1007/978-3-642-10684-2_16
Field of Research 080610 Information Systems Organisation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2009, Springer-Verlag Berlin Heidelberg
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Created: Mon, 07 Jun 2010, 10:35:12 EST by Linda Aldridge

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